In statistics, a maximum-entropy Markov model (MEMM), or conditional Markov model (CMM), is a graphical model for sequence labeling that combines features...

A hidden Markov model (HMM) is a Markov model in which the observations are dependent on a latent (or hidden) Markov process (referred to as X {\displaystyle...

Hierarchical hidden Markov model Maximum-entropy Markov model Variable-order Markov model Markov renewal process Markov chain mixing time Markov kernel Piecewise-deterministic...

The principle of maximum entropy states that the probability distribution which best represents the current state of knowledge about a system is the one...

management module, a term in color management Conditional Markov model or maximum-entropy Markov model Coordinate-measuring machine, a device for dimensional...

will not be encrypted at all. A common way to define entropy for text is based on the Markov model of text. For an order-0 source (each character is selected...

example, a maximum entropy rate criterion may be used for feature selection in machine learning. Information source (mathematics) Markov information...

Maximum entropy classifier – redirects to Logistic regression Maximum-entropy Markov model Maximum entropy method – redirects to Principle of maximum...

regression, multinomial logit (mlogit), the maximum entropy (MaxEnt) classifier, and the conditional maximum entropy model. Multinomial logistic regression is...

statistical models used for sequence labeling. Other common models in use are the maximum entropy Markov model and conditional random field. Artificial intelligence...

random walk Markov chain Examples of Markov chains Detailed balance Markov property Hidden Markov model Maximum-entropy Markov model Markov chain mixing...

hidden Markov model known as a Markov random field, typically with a 'sliding window' or clique size ranging between four and six tokens. Maximum-entropy Markov...

Support Vector Machines Decision Tree Learning Random Forest Maximum-entropy Markov models Conditional random fields Suppose the input data is x ∈ { 1...

and probability, a Markov random field (MRF), Markov network or undirected graphical model is a set of random variables having a Markov property described...

In statistics, Markov chain Monte Carlo (MCMC) is a class of algorithms used to draw samples from a probability distribution. Given a probability distribution...

maximum entropy models such as Multinomial logistic regression Sequence models Recurrent neural network Hidden Markov model Conditional Markov model (CMM)...

applied to undirected, and possibly cyclic, graphs such as Markov networks. Suppose we want to model the dependencies between three variables: the sprinkler...

thermodynamic model to the universe in general. Although entropy does increase in the model of an expanding universe, the maximum possible entropy rises much...

language models, cross-entropy is generally the preferred metric over entropy. The underlying principle is that a lower BPW is indicative of a model's enhanced...

theorem Maximum entropy Markov model (MEMM) Lafferty, J.; McCallum, A.; Pereira, F. (2001). "Conditional random fields: Probabilistic models for segmenting...

among models. When trying to fit parametrized models to data there are various estimators which attempt to minimize relative entropy, such as maximum likelihood...

network Markov model Markov random field Markovian discrimination Maximum-entropy Markov model Multi-armed bandit Multi-task learning Multilinear subspace learning...

prior) to more sophisticated models, such as Good–Turing discounting or back-off models. Maximum entropy language models encode the relationship between...

maximizes entropy (minimizes added information), and in this sense makes the fewest assumptions of the data being modeled; see § Maximum entropy. The parameters...

probability, the filters, random fields, and maximum entropy (FRAME) model is a Markov random field model (or a Gibbs distribution) of stationary spatial...

_{2}p+(1-p)\log _{2}(1-p)}{p}}} Given a mean, the geometric distribution is the maximum entropy probability distribution of all discrete probability distributions...

calculated entropy of the sample. The method gives very accurate results, but it is limited to calculations of random sequences modeled as Markov chains of...

that the latter do not assume knowledge of an exact mathematical model of the Markov decision process, and they target large MDPs where exact methods...

Markov chain, instead of assuming that they are independent identically distributed random variables. The resulting model is termed a hidden Markov model...

maximum entropy spectral estimation. Other possible approaches to estimation include maximum likelihood estimation. Two distinct variants of maximum likelihood...